Data visualization

.title[
# Data visualization
]
.subtitle[
## Course introduction
]
.author[
### Marta Coronado Zamora; Jose F. Sánchez-Herrero; Miriam Merenciano
]
.date[
### 12 February 2026
]

---

## Keep in touch

#### Theory lessons

| Marta Coronado Zamora | Jose F. Sánchez | 
|:-:|:-:|
| <a href="mailto:Marta.coronado@uab.cat"> marta.coronado@uab.cat</a> | <a href="mailto:JoseFrancisco.Sanchez@uab.cat">&nbsp; josefrancisco.sanchez@uab.cat</a> | 
| <a href="https://bsky.app/profile/geneticament.bsky.social">&nbsp; @geneticament</a> | <a href="https://twitter.com/JFSanchezBioinf">&nbsp; @JFSanchezBioinf</a> |
| <a href="https://portalrecerca.uab.cat/es/organisations/grup-de-gen%C3%B2mica-bioinform%C3%A0tica-i-biologia-evolutiva-gbbe/">&nbsp; Universitat Autònoma de Barcelona </a> | <a href="http://www.germanstrias.org/technology-services/genomica-bioinformatica/"> Germans Trias i Pujol Research Institute (IGTP)</a> |

#### Practical lessons

| Miriam Merenciano |
|:-:|
| <a href="mailto:miriam.merenciano@uab.cat"> miriam.merenciano@uab.cat </a> | 
| <a href="https://portalrecerca.uab.cat/es/organisations/grup-de-gen%C3%B2mica-bioinform%C3%A0tica-i-biologia-evolutiva-gbbe/">&nbsp; Universitat Autònoma de Barcelona </a> |

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# Course overview

**P0** | Course Introduction. (Marta Coronado Zamora & Miriam Merenciano)

--
**T1** | Introduction. Perception, illusions, inter-individual variability, ranking of visual features and common pitfalls (Marta Coronado)

--
**Part 1: Tools for data visualization** (Marta Coronado Zamora and Miriam Merenciano) 
**T2** | Basic tools for data visualization (`ggplot2`) - P1, P2, P3 (first assignment) 
**T3** | Dynamic and interactive (`plotly`, `shiny`) - P4, P5 (second assignment)

--
**Part 2: Complex data and dimensionality reduction** (Jose F. Sánchez-Herrero) 
**T4** | Introduction: Visualization for exploring complex data & Dimensionality reduction - P6 
**T5** | Principal component analysis - P7 
**T6** | Non-linear projections: t-SNE - P8 (third assignment) 
**T7** | Non-linear projections: UMAP P9 (fourth assignment)

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# Evaluation

- **10% participation**

Individual submission at the end of each theory/practical session

--
- **40% group assignments** (minimum grade 4/10)

4 assignments, each 10%

- **20% mid-term exam**

- **30% final exam**

The weighted grade of the midterm exam and the final exam requires a minimum score of 3.5 out of 10 to consider the other parts of the evaluation. 
 
 
 
 [Information in the Syllabus](https://www.fib.upc.edu/en/studies/bachelors-degrees/bachelor-degree-bioinformatics/curriculum/syllabus/DV-BBI)
 
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# Course configuration

--
- **Theoretical sessions**: 12 sessions, Friday: 14 - 16h (A5202)

- **Practical exercises**: 13 sessions, 2 groups. Thursday: 14 - 18h (A5201)
  
  Complete and submit to [ATENeA](https://atenea.upc.edu/course/view.php?id=105474) - individual submission

- **Home assignments:**
  
  Work in groups to create analyze data, create visualization, interactive reports and apps... 
  
  Complete and submit to [ATENeA](https://atenea.upc.edu/course/view.php?id=95546) - submission by group

- **Seminars**:
  Invited seminars either in person or online, live or broadcast.

- 27th March | **Clara Inserte**: `clevRvis`: visualization techniques for clonal evolution
  
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# Group assignments

- __1\.__ Create groups of 3-4 people

Make groups of 3-4 students to work on the home assignments projects.

- __2\.__ Enroll in ATENeA

https://atenea.upc.edu/mod/choicegroup/view.php?id=5311480

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# Previous capacities

Basic knowledge in R and familiarity with RStudio are prerequisites.

2. Introduce the event code: **BJMEAL**

3. Login (SSO) with ATENeA
]

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# Let's get started!
## **Tools for  data visualization**

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## Type of tools

### Two main types:

- Graphical user interface (GUI)

Many examples: Perseus computational platform, Cytoscape, Blast2GO, Gephi, ... 
 
--

- Code-based

R (and other computer languages [Python])

- Interactive code-based `R`

RStudio (Jupyter Notebook, and other computer languages)

[Wide range...](https://en.wikipedia.org/wiki/List_of_information_graphics_software)

--

<div style="background-color:#FFDA9E">
&nbsp;Question
 
What prons and cons do you think GUI tools have in comparison to code-based? 
</div>

---

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## Slides with Rmarkdown

### **Interactive documents** 
`R code` can be executed within RStudio!

``` r
value <- 2
value + 3
```

```
## [1] 5
```

--
.pull-left[

``` r
# ggplot2 boxplot
ggplot(data = iris, 
 mapping = aes(x = Species,
 y = Petal.Length,
 fill = Species)) +
 geom_boxplot()
```
]
.pull-right[
<img src="data:image/png;base64,#0_files/figure-html/unnamed-chunk-3-1.png" width="324" style="display: block; margin: auto;" />
]
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## Visualization libraries in `R`

- `base`
- `grid`: `lattice` and `ggplot2`

--

<div align="left" style="background-color:#FFDA9E">
&nbsp;Question
 
Describe the graphics. In your opinion, which do you think is the simplest? and the most complex? do you think the code to generate the figures reflect the complexity?
</div>

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## Visualization libraries in `R`

``` r
# base
hist(iris$Sepal.Width)

# ggplot2
ggplot(iris, aes(Sepal.Width)) +
  geom_histogram()
```

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## Visualization libraries in `R`

``` r
# base
plot(circumference ~ age,
     data=Orange[Orange$Tree %in% "4", ], type = "l",
     main = "Base - complex")
points(circumference ~ age, col="darkred",
       data=Orange[Orange$Tree %in% "2", ], type = "l")
points(circumference ~ age, col="orange",
       data=Orange[Orange$Tree %in% "5", ], type = "l")
points(circumference ~ age, col="yellow",
       data=Orange[Orange$Tree %in% "1", ], type = "l")
points(circumference ~ age, col="darkgreen",
       data=Orange[Orange$Tree %in% "3", ], type = "l")
legend("topleft",
       c("4", "2", "5", "1", "3"), title="Tree",
       col=c("black", "darkred", "darkorange", "yellow", "darkgreen"),
       lty=c(1, 1, 1, 1, 1))
```

``` r
# ggplot2
ggplot(Orange, aes(age, circumference,
      colour = Tree)) + geom_line() +
      labs(title = "ggplot2 - complex")
```
]

--
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<img src="data:image/png;base64,#0_files/figure-html/unnamed-chunk-9-1.png" width="360" style="display: block; margin: auto;" />

<img src="data:image/png;base64,#0_files/figure-html/unnamed-chunk-10-1.png" width="360" style="display: block; margin: auto;" />
]

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### Other visualization libraries

(Outside our scope)

- Python
    + `matplotlib`, `seaborn`
    + `Bokeh`, `pygal`
- Java: `Processing`
- Javascript: `D3.js`

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# Basic `R` knowledge

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### Installing a package

``` r
# Download and install a package from CRAN
install.packages("ggplot2")

# Download and install a package from GitHub(you need the devtools library installed)
devtools::install_github("yihui/xaringan")
```

### Loading a package

``` r
# Load the library to the current session
library("ggplot2")
library("xaringan")
```

### Loading data

``` r
# Loading a tab-separated file with a header
data <- read.table("data.txt", header = TRUE, sep = "\t")
```

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## Data types and structures

Main data types (other will not be discussed: _complex_ and _raw_):

- __Logical__: can only take on two values: true (`TRUE`, `T`) or false (`FALSE`, `F`)
- __Numeric__: real or decimal (`2`, `15.5`)
- __Integer__: `2L` (the `L` tells R to store this as an integer)
- __Character__: any type of character or number (`"a"`, `"swc"`, `"2"`)

To know the data type, you can use the `class()` function.

``` r
type_list <- list(TRUE, 1.2, 10L, "a")
sapply(type_list, class)
```

```
## [1] "logical"   "numeric"   "integer"   "character"
```

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## Data types and structures

Elements of the previous data types may be combined to form data structures. Main structures:

- __Vector__: collection of elements that holds data of a single data type
- __Matrix__: vector with dimensions (the number of rows and columns)
- __Factor__: to deal with categorical variables
- __List__: a special type of vector where each element can be a different type
- __Data Frame__ : a special type of list where every element of the list has same length

``` r
# A vector x of mode numeric
x <- c(1, 2, 3)

# A vector y of mode logical
y <- c(TRUE, TRUE, FALSE, FALSE)

# A vector z of mode character
z <- c("ATG", "GGC", "TGA")
```

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## Data types and structures

Elements of the previous data types may be combined to form data structures. Main structures:

``` r
matrix22 <- matrix(
 c(1, 2, 3, 4),
 nrow = 2,
 ncol = 2)
matrix22
```

```
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4
```

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## Data types and structures

Elements of the previous data types may be combined to form data structures. Main structures:

``` r
factor_vector <- as.factor(c("rna", "dna", "dna", "rna"))
factor_vector
```

```
## [1] rna dna dna rna
## Levels: dna rna
```

``` r
str(factor_vector)
```

```
##  Factor w/ 2 levels "dna","rna": 2 1 1 2
```

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## Data types and structures

Elements of the previous data types may be combined to form data structures. Main structures:

``` r
x <- list(1, "a", TRUE, 1+4i)
x
```

```
## [[1]]
## [1] 1
## 
## [[2]]
## [1] "a"
## 
## [[3]]
## [1] TRUE
## 
## [[4]]
## [1] 1+4i
```

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## Data types and structures

Elements of the previous data types may be combined to form data structures. Main structures:

``` r
dat <- data.frame(id = letters[1:10], x = 1:10, y = 11:20)
dat
```

```
##    id  x  y
## 1   a  1 11
## 2   b  2 12
## 3   c  3 13
## 4   d  4 14
## 5   e  5 15
## 6   f  6 16
## 7   g  7 17
## 8   h  8 18
## 9   i  9 19
## 10  j 10 20
```
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# Additional resources

- Other subjects within this degree

- DataCamp courses: https://www.datacamp.com/

- Coursera: https://www.coursera.org/

- W3Schools: https://www.w3schools.com/r/

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### Tidy data

Data frames with one observation per row and one variable per column.

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### Tidy data

Two types of ordered data structures:

- **Wide format** (the most common): in a wide format, multiple measurements of a single observation are stored in a single row.

```
##   Student Math Literature PE
## 1       A   99         45 56
## 2       B   73         78 55
## 3       C   12         96 57
```

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- **Long format**: each row corresponds to one measurement of an observation.

```
## Warning: package 'readr' was built under R version 4.5.2
```

```
## Warning: package 'purrr' was built under R version 4.5.2
```

```
## Warning: package 'stringr' was built under R version 4.5.2
```

```
## # A tibble: 9 × 3
## Student Subject Score
## <chr> <chr> <dbl>
## 1 A Math 99
## 2 A Literature 45
## 3 A PE 56
## 4 B Math 73
## 5 B Literature 78
## 6 B PE 55
## 7 C Math 12
## 8 C Literature 96
## 9 C PE 57
```

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### Tidy data

There are functions to convert from wide format to long format:

``` r
library(tidyr)

long_df <- pivot_longer(
 wide_df,
 cols = c(Math, Literature, PE), # o cols = -c(Student),
 names_to = "Subject",
 values_to = "Score"
 )
long_df
```

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# Getting help

- `?read.table`, `?str`, `?as.factor`
- Press F1 (in RStudio)
- [Stack Overflow](https://stackoverflow.com) ([`R`](https://stackoverflow.com/questions/tagged/r), [`ggplot2`](https://stackoverflow.com/questions/tagged/ggplot2))
- ChatGPT
- Ask your classmates or your teacher

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# Today's task

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# Today's task

1. Make sure **RStudio** is working

2. Make groups of 3-4 people and fill the form at ATENeA

3. Install some packages you will need for this practical sessions:

+ `ggplot2`

+ `tidyr`
  
  + `shiny`
  
  + `plotly`

4. Work in the following exercises

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## Exercise: create some testing plots

Execute the following chunks using the [`iris`](https://en.wikipedia.org/wiki/Iris_flower_data_set) dataset and think what is going on:

``` r
library(ggplot2)
head(iris)
str(iris)
```

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## Exercise: create some testing plots

What figure does the following command generate?

``` r
ggplot(data = iris, mapping = aes(x = Species, y = Petal.Length, fill = Species)) +
  geom_boxplot()
```

Here we can see the distribution of the variable petal length regarding the species it belongs

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## Exercise: create some testing plots

What figure does the following command generate?

``` r
ggplot(data=iris,aes(x=Sepal.Width, y=Sepal.Length, color=Species)) + geom_point() + theme_minimal()
```

--

Here we can see using a **scatter plot**, the variable sepal width and sepal length and coloured regarding the species it belongs

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## Exercise: describe a data set

Read the file in this [link](https://raw.githubusercontent.com/marta-coronado/data_visualization/refs/heads/main/P/0/data/sample.txt), ensure it has a tidy format; indicate the data type of each variable; convert to long format.

Which column(s) will you use in the `cols` argument of the `pivot_longer` function?

You can read directly a file from a link `read.table("https://...txt")`

Solutions in the next slides 😇

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## Exercise: describe a data set

``` r
data <- read.table(file="https://raw.githubusercontent.com/marta-coronado/data_visualization/refs/heads/main/P/0/data/sample.txt", header=T)
head(data)
```

```
##      ID Group     Width   Height    Depth
## 1 ID001     B  6.518109 41.16221 4.637743
## 2 ID002     B  6.505138 44.10542 4.822542
## 3 ID003     A 11.428349 45.03662 5.149416
## 4 ID004     A 13.714852 58.60545 5.834809
## 5 ID005     B  8.802453 47.40527 4.546300
## 6 ID006     B 10.038084 58.58873 5.981818
```

``` r
dim(data)
```

```
## [1] 100   5
```

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## Exercise: describe a data set

Indicate the data type of each variable.

``` r
sapply(data, class)
```

```
##          ID       Group       Width      Height       Depth 
## "character" "character"   "numeric"   "numeric"   "numeric"
```

``` r
str(data)
```

```
## 'data.frame':	100 obs. of  5 variables:
##  $ ID    : chr  "ID001" "ID002" "ID003" "ID004" ...
##  $ Group : chr  "B" "B" "A" "A" ...
##  $ Width : num  6.52 6.51 11.43 13.71 8.8 ...
##  $ Height: num  41.2 44.1 45 58.6 47.4 ...
##  $ Depth : num  4.64 4.82 5.15 5.83 4.55 ...
```

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## Exercise: describe a data set

Convert from wide to long format using `tidyr::pivot_longer()`

``` r
library(tidyr)
long_df <- pivot_longer(
 data,
 cols = -c("ID", "Group"),
 names_to = "metric",
 values_to = "value"
 )
## check dimensions
dim(long_df)
```

```
## [1] 300   4
```

``` r
dim(data)
```

```
## [1] 100   5
```

---

## Exercise: describe a data set

Convert from wide to long format using `tidyr::pivot_longer()`

``` r
library(tidyr)
long_df <- pivot_longer(
 data,
 cols = -c("ID", "Group"),
 names_to = "metric",
 values_to = "value"
 )

head(long_df)
```

```
## # A tibble: 6 × 4
## ID Group metric value
## <chr> <chr> <chr> <dbl>
## 1 ID001 B Width 6.52
## 2 ID001 B Height 41.2 
## 3 ID001 B Depth 4.64
## 4 ID002 B Width 6.51
## 5 ID002 B Height 44.1 
## 6 ID002 B Depth 4.82
```

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## See you next day

### Keep in touch

#### Theory lessons

#### Practical lessons